Python-based
Hierarchical
ENvironment
for
Integrated
Xtallography
Process model files produced by AlphaFold, RoseTTAFold and other prediction software, replacing information these programs put in the B-factor field with pseudo-B values and optionally breaking the model into compact domains.
Structure prediction software is now capable of generating models that are highly accurate over some or all parts of the models. Importantly, these predictions often come with reliable residue-by-residue estimates of uncertainty.
Compact domains in these predicted models in which all the residues have high confidence often will be very accurate over the entire domains. However, separate domains that each have high confidence but are connected by lower confidence residues sometimes have relative positions and orientations that differ between predicted and experimentally-determined structures.
When using predicted models as a starting point for experimental structure determination, it can be helpful to:
Remove low-confidence residues entirely Break up the model into domains and allow the domains to have different orientations
For a high-confidence predicted model, you might try using the predicted model as-is first. For most predicted models, you may want to try removing low-confidence residues, then additionally try breaking the model into domains and placing the domains one at a time.
The process_predicted_model tool uses estimates of uncertainty supplied by structure prediction tools in the B-value (atomic displacement parameters) field of a model to create new pseudo-B values, to remove uncertain parts of the model, and to break up the model into domains.
The B-value field in most predicted models represents one of three possible values:
An actual B-value (atomic displacement parameter) An estimate of error in A (rmsd) A confidence (LDDT) on a scale of either 0 to 1 or 0 to 100.
In process_predicted_model, error estimates in A or confidence values are first converted to B-values. Then residues with high B-values are removed. Then the remaining residues are grouped (optionally) into domains.
Error estimates in A are converted to B-values using the standard formula for the relationshiop between rms positional variation and B-values:
B = rmsd**2 * ((8 * (pi**2)) / 3.0)
LDDT values are first converted to a scale of 0 to 1. You can specify whether the LDDT values in your model are from 0 to 1 (fractional) or from 0 to 100. If you don't specify, a model with all LDDT values between 0 and 1 is assumed to contain fractional LDDT values.
Then LDDT values on a scale of 0 to 1 are converted to error estimates using an empirical formula from
This empirical formula is:
RMSD = 1.5 * exp(4*(0.7-LDDT))
Normally it is a good idea to remove low-confidence regions from a predicted model before using them as a starting point for experimental structure determination. For AlphaFold2 models, low-confidence corresponds approximately to an LDDT value of about 0.7 (on a scale of 0 to 1, or 70 on a scale of 0 to 100), or to an RMSD value of about 1.5, or to a B-value of about 60. For other types of models these values might vary, so you might need to experiment or use values that others have found useful.
After trimming low-confidence residues, you will usually be left with a model that has some complete parts of various sizes and some small pieces.
It can be helpful to group the pieces from your trimmed model into compact domains, or even to split some pieces into compact domains. The process_predicted_model tool allows you to choose a typical domain size, and if you want, a maximum number of domains, and then it will try to split your model into compact domains.
There are two methods available. One is based finding compact domains, the other is based on using the predicted alignment error matrix (AlphaFold2 only).
The method used is to calculate a low-resolution map based on the input model, then to identify large blobs corresponding to domains. All the residues in the structure are assigned to an initial domain.Then the residues are regrouped in order to have as few cases where small parts of the model are part of one domain but neighboring parts are part of another as possible.
When using this method, you can easily adjust the number of domains you get by adjusting the target domain size (in A). You can also just restrict the number using the maximum_domains keyword (less good).
This method analyzes the predicted alignment error matrix (PAE) provided by AlphaFold2 and finds groupings of residues that have small mutual alignment error. This often corresponds to domains.
When using this method you can adjust the number of domains by changing the value of pae_power (the exponent applied to pae before using it in finding domains). You can also just restrict the number using the maximum_domains keyword (less good).
Running process_predicted_model is easy. From the command-line you can type:
phenix.process_predicted_model my_model.pdb b_value_field_is=lddt
This will convert the B-value field in my_model.pdb based from LDDT to B-values, trim residues with LDDT less than 0.7, and write out a new model with individual chains (separate chain ID values) corresponding to compact domains.